Yet Another Intermediate-Level Attack

• URL: http://arxiv.org/abs/2008.08847v1
• Date: Thu, 20 Aug 2020 09:14:04 GMT
• Title: Yet Another Intermediate-Level Attack
• Authors: Qizhang Li, Yiwen Guo, Hao Chen
• Abstract summary: The transferability of adversarial examples across deep neural network (DNN) models is the crux of a spectrum of black-box attacks. We propose a novel method to enhance the black-box transferability of baseline adversarial examples.
• Score: 31.055720988792416
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The transferability of adversarial examples across deep neural network (DNN) models is the crux of a spectrum of black-box attacks. In this paper, we propose a novel method to enhance the black-box transferability of baseline adversarial examples. By establishing a linear mapping of the intermediate-level discrepancies (between a set of adversarial inputs and their benign counterparts) for predicting the evoked adversarial loss, we aim to take full advantage of the optimization procedure of multi-step baseline attacks. We conducted extensive experiments to verify the effectiveness of our method on CIFAR-100 and ImageNet. Experimental results demonstrate that it outperforms previous state-of-the-arts considerably. Our code is at https://github.com/qizhangli/ila-plus-plus.

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